Picture of refinery

Increasingly, organisations are recognising the importance of good data management and how it can improve organisational performance. Managing data across large (and small) enterprises can be challenging, there are a range of standards and approaches that provide guidance applicable to many sectors.

Asset intensive organisations include refineries, power stations, substations, railways, water treatment and distribution networks, highways etc. Asset owning organisations typically have large portfolios of assets with huge variety of types, construction and configuration, ages, condition and performance. Such organisations need to manage their complex portfolio of assets over extended periods of time.

Why is data frequently stated as one of the top 3 challenges for asset intensive organisations?
What approaches to data work best for asset intensive organisations?

Some of the relevant standards and approaches for asset intensive organisations include:

  • The ISO 55000 family of standards defines the management system for managing assets describing some of the key purposes where data supports asset management, for example, developing a long term plan of interventions (or jobs) to be done on your assets
  • The ISO 19650 series and related approaches are widely applicable in the built environment and describe how to ‘do’ BIM (correctly Building Information Modelling, but can also be thought of as Better Information Management)
  • ISO 8000-61 which describe how to ‘do’ data quality management and is applicable to virtually all contexts
  • ISO 38505 describes approaches to data governance based on ISO 38500

If there are all these standards and many sources of related guidance, why is asset information difficult?

Asset information challenges

There are a number of ways in which asset information is harder to manage than, say, retail data:

  • The nature of the data subjects (the assets)
  • The nature of the data
  • Data requirements

These challenges are explored in more detail below.

Pipework and stairs at steelworks

Longevity

The data needs to exist and be usable for at least as long as the asset exists – for many buildings and civil structures, the asset life could be 50-150 years, therefore, the asset data needs to be usable (and survive!) many changes of platform and technology.

Complexity

Assets are complex, the data that relates to them is just as complex – assets often have complex relationships to other assets, for example, a motor is part of a hydraulic power pack on a machine that is part of an automation system for a larger system. The motor is powered from a central distribution board and controlled by a local PLC (Programmable Logic Controller) that in turn is controlled by a central control system.

The asset information includes information about what the motor is and how it is classified, control logic diagrams, drawings of the overall machine, Operation and Maintenance manuals about how to correctly look after the machine, site layout and services drawings etc. A seemingly simple modification of an asset, for example, altering the route of pipework, may require updates to multiple sources of asset information.

Differing requirements

Many information users have differing requirements:

  • Operators need to know the capability and availability of the motor;
  • Maintainers need to know the maintenance history, reliability and manuals for the motor;
  • Finance need to know the value of the motor and its age for depreciation purposes; and
  • Asset managers need to understand the condition and rate of deterioration of the asset, its future demand and future strategic plans.

Overall, this is a complex set of requirements impacting the various stakeholders in different ways.

Rails and ballast

Location and connectivity

Linear assets, such as rail track, highways and transmissions lines present a range of additional challenges:

  • Understanding the location and extent of assets may require extensive surveys and agreement on the underlying spatial datum
  • A technique called ‘linear referencing‘ may be needed to locate assets and features along the length of a linear asset. This will also support linear attributes, such as gradient and speed limit, which will vary along the length of an asset
  • Knowledge of connectivity is essential – just because two assets are adjacent or cross each other, does not necessarily mean that they are connected. Understanding connectivity is essential to understand overall system configurations

Agreeing the approach to be adopted for each of these three spatial considerations may take some time, but it is essential that careful consideration is made to avoid unnecessarily expensive surveys and data capture.

Data capture

Asset information cannot easily be recreated when it is about a buried cable or pipe. The only way to replace lost data could well be to excavate down to the asset. For some assets, such as cables, even when the asset is located, it will be extremely difficult to understand exactly what it is without some form of destructive analysis. The history, test certificates and related documents will be almost impossible to recreate for many assets.

Details about complex structures cannot easily be regained, for example, details of the reinforcing used for a concrete structure will be almost impossible to determine without destroying the asset. Laser surveys can produce a very detailed and accurate view of the surface of assets in a space, however, they are unable to determine what is below the surface of object – a long cylindrical object running through a plant room could be a pipe, HV cable or be part of the building structure.

Checking accuracy

Assessing the accuracy of data requires a check between the ‘thing’ represented by the data (or a valid surrogate for the asset) and the data itself – just because the data exists, is valid and plausible does not necessarily mean that it is right!

Checking asset information can be difficult/ expensive/ risky as assets may be either inaccessible (for example buried, at height, behind safety guards or acoustic cladding etc.) or perhaps require a customer to be present to allow you access to assets on their site. Gaining access to assets in order to check data accuracy may require a permit to work, a plant/ site shutdown or suitable PPE if it is located in a dangerous environment. Additionally, assets may be dispersed over a wide geographic area requiring much travel time just to reach the asset.

When checking the accuracy of an asset inventory (the full asset list), assessments need to be performed in two distinct directions:

  • Checking that every data entry relates to only one asset i.e. checking that there are no ‘ghost’ assets; and
  • Checking that every asset has only one data entry relating to it, i.e. there is no duplication.
Electricity substation

Interoperability

Organisations do not work in isolation and often collaborate with a range of partners and share data with others, similarly, contractors usually work with many clients. Yet in both cases, differences in data structure and standards increase the ‘friction’ needed to collaborate and share data. Contractors and suppliers have to understand and work with a wide range of data standards and approaches simultaneously thereby increasing costs and reducing efficiency.

Adoption of different standards, for example if a unified data standard was agreed, could take many years to deliver. In part due to software systems needing to be changed to enable this, but mainly due to there being minimal immediate benefits to offset the costs of data migration.

Security

In the built environment, security considerations are far wider than cyber security/ anti-malware. Some assets are sensitive (i.e. whose failure would lead to significant adverse impacts to the organisation and society) and some asset data is sensitive (e.g. commercially sensitive data, proprietary design information etc.) yet asset owners may want to collaborate with their supply chains, so how can this be done securely? Key information risks include:

  • Availability of data enabling ‘hostile reconnaissance‘ i.e. allowing malicious actors to plan hostile acts remotely
  • Loss of commercially sensitive information, for example, contract prices, day rates, project position etc.
  • Loss of intellectual property, for example, design information, programme logic etc.
  • Data aggregation risk, for example, data combined with other data sets may enable other hostile acts;
  • Neighbouring assets, for example, a utility survey of your site may show your neighbours sensitive assets and vice versa.

A security minded approach can help avoid many of these problems.

Decommissioning

Safe decommissioning, dismantling, demolition and disposal of assets requires information about the materials and construction of the assets and perhaps the designers specific intent about how a facility could be decommissioned. The value in such data generally comes at the end of the life of a facility or asset, so ‘house keeping’ to delete data of no apparent value may inadvertently remove data that will have key future value. The challenges of nuclear decommissioning, for example, are exacerbated where records were not kept or retained on the history of a facility.

Cars on a highway

Managing asset information

Managing asset information effectively requires broad understanding of the challenges illustrated above coupled with the ability to apply this understanding and ensure organisations ‘do the right’ thing regarding asset data.

Some of the standards mentioned at the beginning of this article can help define approaches to the effective management of asset information. Knowledge of good practice approaches needs to be blended with the methods and processes already used by an organisation to develop appropriate and effective solutions that deliver required outputs. It is important that a suitably pragmatic approach is taken to ensure that changes are delivered without excessive costs and employee resistance.

Governance

Governance is the establishment of suitable oversight and control of the activities of an organisation. Data governance should be in place across all these asset data activities to cover three key areas:

  • Direction – strategy, policy, process and data definition
  • Monitoring – data quality assessment and reporting
  • Evaluation – assessing organisational and technology capabilities against good practice approaches

Above all, governance should ensure that there is suitable senior level oversight of the data related activities of the organisation. Again, pragmatic approaches to implementation are essential to support effective adoption.

DPA support

How can we support you in your asset data challenges?

  • DPA have many years experience working with a variety of organisations across a range of asset intensive sectors to help them manage their asset information effectively
  • We are some of the leading asset data practitioners in the UK and have worked both as consultants and directly for a range of asset intensive organisations
  • We are experienced at developing and implementing suitable requirements, processes, procedures, governance and culture change to help overcome your asset data challenges
  • Our pragmatic approach and ability to engage positively with clients raises competence and delivers enduring outcomes
  • We support development of a number of British and ISO standards relating to data and assets.

Get in contact to have a chat to explore how we could help you.

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